import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
## load in the hierarchy information
url = "https://raw.githubusercontent.com/bcaffo/MRIcloudT1volumetrics/master/inst/extdata/multilevel_lookup_table.txt"
multilevel_lookup = pd.read_csv(url, sep = "\t").drop(['Level5'], axis = 1)
multilevel_lookup = multilevel_lookup.rename(columns = {
"modify" : "roi",
"modify.1" : "level4",
"modify.2" : "level3",
"modify.3" : "level2",
"modify.4" : "level1"})
multilevel_lookup = multilevel_lookup[['roi', 'level4', 'level3', 'level2', 'level1']]
## Now load in the subject data
id = 127
subjectData = pd.read_csv("https://raw.githubusercontent.com/smart-stats/ds4bio_book/main/book/assetts/kirby21AllLevels.csv")
subjectData = subjectData.loc[(subjectData.type == 1) & (subjectData.level == 5) & (subjectData.id == id)]
subjectData = subjectData[['roi', 'volume']]
## Merge the subject data with the multilevel data
subjectData = pd.merge(subjectData, multilevel_lookup, on = "roi")
subjectData = subjectData.assign(icv = "ICV")
subjectData = subjectData.assign(comp = subjectData.volume / np.sum(subjectData.volume))
df = subjectData.copy()
categories = ['icv','level1', 'level2', 'level3', 'level4','roi']
newDf = pd.DataFrame()
for i in range(len(categories)-1):
tempDf = df[[categories[i],categories[i+1],'comp']]
tempDf.columns = ['source','target','count']
newDf = pd.concat([newDf,tempDf])
newDf = newDf.groupby(['source','target']).agg({'count':'sum'}).reset_index()
label_list = list(np.unique(df[categories].values))
source = newDf['source'].apply(lambda x: label_list.index(x))
target = newDf['target'].apply(lambda x: label_list.index(x))
count = newDf['count']
fig = go.Figure(data=[go.Sankey(
node = dict(
pad = 1,
thickness = 4,
line = dict(color = "black", width = 0.5),
label = label_list,
color = "blue"
),
link = {"source": source, "target": target, "value": count}
)])
fig.update_layout(font_size=4)
fig.show()